from agenthub.planner_agent.response_parser import PlannerResponseParser
from opendevin.controller.agent import Agent
from opendevin.controller.state.state import State
from opendevin.core.message import ImageContent, Message, TextContent
from opendevin.events.action import Action, AgentFinishAction
from opendevin.llm.llm import LLM
from opendevin.runtime.tools import RuntimeTool

from .prompt import get_prompt_and_images


class PlannerAgent(Agent):
    VERSION = '1.0'
    """
    The planner agent utilizes a special prompting strategy to create long term plans for solving problems.
    The agent is given its previous action-observation pairs, current task, and hint based on last action taken at every step.
    """
    runtime_tools: list[RuntimeTool] = [RuntimeTool.BROWSER]
    response_parser = PlannerResponseParser()

    def __init__(self, llm: LLM):
        """Initialize the Planner Agent with an LLM

        Parameters:
        - llm (LLM): The llm to be used by this agent
        """
        super().__init__(llm)

    def step(self, state: State) -> Action:
        """Checks to see if current step is completed, returns AgentFinishAction if True.
        Otherwise, creates a plan prompt and sends to model for inference, returning the result as the next action.

        Parameters:
        - state (State): The current state given the previous actions and observations

        Returns:
        - AgentFinishAction: If the last state was 'completed', 'verified', or 'abandoned'
        - Action: The next action to take based on llm response
        """
        if state.root_task.state in [
            'completed',
            'verified',
            'abandoned',
        ]:
            return AgentFinishAction()

        prompt, image_urls = get_prompt_and_images(
            state, self.llm.config.max_message_chars
        )
        content = [TextContent(text=prompt)]
        if image_urls:
            content.append(ImageContent(image_urls=image_urls))
        message = Message(role='user', content=content)
        resp = self.llm.completion(messages=[message.model_dump()])
        return self.response_parser.parse(resp)
